Four decades of counterfeit research: A bibliometric analysis
Bibliographic record
Abstract
This paper assesses the evolution of last 43 years in counterfeit research with respect to sources of knowledge (i.e.journals, authors, institutions, countries) and research themes.The oldest paper on this subject discovered in the Scopus database was published 43 years ago, yet a time frame was not specified.Sources of knowledge are assessed on research productivity (quantitative) as well as impact (qualitative).Research themes, key areas of focus within the counterfeit research landscape, are identified and discussed to conceptualize our understanding of the field.Via a systematic literature review, 713 peer-reviewed academic articles published in 282 journals from 1978 to 2021 were selected as the sample for this study.The systematic review technique was chosen as compared with narrative reviews of the literature it focuses on open, extensive, and detailed approaches to literature searches, in addition to conforming to the scientific criteria utilised in primary research, namely transparency, rigour, comprehensiveness, and reproducibility.A database of references and citations was created for analysis.The data was analyzed to prepare comparative tables.Further, the Leximancer software was used to generate lexical conceptual trends.This data was further analyzed to identify emerging themes.The Journal of Business Ethics had the highest number of articles and citations, followed by the Journal of Business Research and Business Horizons.Ian Phau (14 articles) and Michael D. Smith, (9 articles) were the most prolific authors.Joseph Nunes and Ian Phau attained the highest number of citations, cited 658 and 577 times respectively.Eight major research themes were identified: products, piracy, model, price, firms, digital, supply, and ethical.Each theme was analyzed over time.The major research areas analyzed across the articles over time were Technology (particularly "Technology" and "Software" topics) and Ethics (particularly "IP" and "Legislation").The identification of these research area captures the essence of the paper's uniqueness and contribution to this field of research.This is the first systematic literature review in counterfeit literature that captures multi-decade sources of knowledge in business journals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.124 | 0.156 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".